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Misalignment is not required for an agent to act without authorization

A response post argues that misalignment is not required for an AI agent to act without authorization, because the real problem is inference itself generating values, conditions, or intent for execution. The author's framework, execution-state-preflight, blocks agents from using inference to synthesize execution values, relying instead on explicit provenance lookups and slot-based preconditions outside the model, and halting execution with an unresolved route record when any required parameter is unresolved. The author rejects confidence scores and threshold-based evaluations because scoring metrics let the model fill in blanks up to a threshold, and instead reads only a deterministic verdict record generated outside the model context.

read1 min views1 publishedSep 26, 2026

Hi Timothy, thanks for the thoughtful response and for sharing your work.

I’d like to clarify a fundamental distinction between your perspective and the core thesis of my post (and the accompanying specification, execution-state-preflight).

My framework does not deal with or accommodate “progressive determination” (the forward propagation of unauthorized assumptions), simply because it is designed to prevent that process from ever starting.

The central objective is to completely block the agent from using inference to synthesize values, conditions, or intent for execution. Instead of allowing the model to fill in missing slots, execution relies strictly on explicit provenance lookups and slot-based preconditions outside the model. If a single required parameter remains unresolved, the system halts execution and emits an unresolved route record rather than guessing.

This is precisely why my approach avoids using confidence scores or threshold-based evaluations. Scoring metrics inherently grant the model room to “fill in the blanks” through inference up to a certain threshold. In contrast, execution in my architecture only reads a deterministic verdict record generated outside the model context—it never evaluates a conditional on the fly.

Therefore, the core problem is not how an unauthorized assumption propagates forward over time, but rather how to seal the channel where values and intents are generated via inference in the first place.

Thanks again for linking your paper on Zenodo—I look forward to reading your semiotic account of LLMs!

Best regards,

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